# Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Study

arXiv: https://arxiv.org/abs/2410.20792 · HTML: https://mlsanity.com/paper/2410.20792/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 28 Oct 2024.

## Builds on (most cited first, 15 of 15)

- [Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining](https://mlsanity.com/paper/2409.14327/) (2024)
- [Research on Adverse Drug Reaction Prediction Model Combining Knowledge Graph Embedding and Deep Learning](https://mlsanity.com/paper/2407.16715/) (2024)
- [Adaptive Friction in Deep Learning: Enhancing Optimizers with Sigmoid and Tanh Function](https://mlsanity.com/paper/2408.11839/) (2024)
- [Breast Cancer Image Classification Method Based on Deep Transfer Learning](https://mlsanity.com/paper/2404.09226/) (2024)
- [Dynamic Hypergraph-Enhanced Prediction of Sequential Medical Visits](https://mlsanity.com/paper/2408.07084/) (2024)
- [A Lightweight GAN-Based Image Fusion Algorithm for Visible and Infrared Images](https://mlsanity.com/paper/2409.15332/) (2024)
- [Algorithm Research of ELMo Word Embedding and Deep Learning Multimodal Transformer in Image Description](https://mlsanity.com/paper/2408.06357/) (2024)
- [Adversarial Neural Networks in Medical Imaging Advancements and Challenges in Semantic Segmentation](https://mlsanity.com/paper/2410.13099/) (2024)
- [Applying Conditional Generative Adversarial Networks for Imaging Diagnosis](https://mlsanity.com/paper/2408.02074/) (2024)
- [Enhancing Convolutional Neural Networks with Higher-Order Numerical Difference Methods](https://mlsanity.com/paper/2409.04977/) (2024)
- [Contrastive Learning for Knowledge-Based Question Generation in Large Language Models](https://mlsanity.com/paper/2409.13994/) (2024)
- [Reducing Bias in Deep Learning Optimization: The RSGDM Approach](https://mlsanity.com/paper/2409.15314/) (2024)
- [Optimizing News Text Classification with Bi-LSTM and Attention Mechanism for Efficient Data Processing](https://mlsanity.com/paper/2409.15576/) (2024)
- [Comparative Analysis of Open-Source Language Models in Summarizing Medical Text Data](https://mlsanity.com/paper/2405.16295/) (2024)
- [Summarizing Radiology Reports Findings into Impressions](https://mlsanity.com/paper/2405.06802/) (2024)

## Built on by (most cited first, 8 of 8)

- [Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learning](https://mlsanity.com/paper/2411.06326/) (2024)
- [Adaptive Optimization for Enhanced Efficiency in Large-Scale Language Model Training](https://mlsanity.com/paper/2412.04718/) (2024)
- [Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification](https://mlsanity.com/paper/2411.12151/) (2024)
- [An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction](https://mlsanity.com/paper/2412.02211/) (2024)
- [A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation](https://mlsanity.com/paper/2411.12157/) (2024)
- [AI-Driven Health Monitoring of Distributed Computing Architecture: Insights from XGBoost and SHAP](https://mlsanity.com/paper/2501.14745/) (2024)
- [Optimizing Multi-Task Learning for Enhanced Performance in Large Language Models](https://mlsanity.com/paper/2412.06249/) (2024)
- [Dynamic User Interface Generation for Enhanced Human-Computer Interaction Using Variational Autoencoders](https://mlsanity.com/paper/2412.14521/) (2024)

## Abstract (arXiv)

This paper proposes a medical literature summary generation method based on the BERT model to address the challenges brought by the current explosion of medical information. By fine-tuning and optimizing the BERT model, we develop an efficient summary generation system that can quickly extract key information from medical literature and generate coherent, accurate summaries. In the experiment, we compared various models, including Seq-Seq, Attention, Transformer, and BERT, and demonstrated that the improved BERT model offers significant advantages in the Rouge and Recall metrics. Furthermore, the results of this study highlight the potential of knowledge distillation techniques to further enhance model performance. The system has demonstrated strong versatility and efficiency in practical applications, offering a reliable tool for the rapid screening and analysis of medical literature.